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Answer: Google Cloud Bigtable
The question describes a high-throughput, real-time weather application with 50,000 sensors each sending 10 readings per second (totaling 500,000 writes per second), with data in timestamp and sensor reading format. This is a classic time-series IoT use case requiring low-latency reads for real-time charting. Google Cloud Bigtable (option C) is the optimal choice because it is a NoSQL database designed for high-throughput, low-latency workloads, excels at handling time-series data, and scales horizontally to manage massive write volumes. The community discussion strongly supports this, with 100% consensus (C) and upvoted comments highlighting Bigtable's suitability for IoT, time-series data, and real-time requirements. Other options are less suitable: BigQuery (A) is for analytics, not real-time writes; Cloud SQL (B) is a relational database with lower write scalability; Cloud Storage (D) is for object storage, not real-time querying.
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You are building a high-performance, real-time weather charting application that requires accurate data. The data is ingested from 50,000 sensors, each transmitting 10 readings per second in the format of a timestamp and a sensor reading. Where should you store this data?
A
Google BigQuery
B
Google Cloud SQL
C
Google Cloud Bigtable
D
Google Cloud Storage